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- Yamamoto Kohei
- Graduate School of Information Science and Engineering, Ritsumeikan University
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- Kan Fumiya
- College of Information Science and Engineering, Ritsumeikan University
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- Murao Kazuya
- College of Information Science and Engineering, Ritsumeikan University
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- Mochizuki Masahiro
- Research Organization of Science and Technology, Ritsumeikan University
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- Nishio Nobuhiko
- College of Information Science and Engineering, Ritsumeikan University
この論文をさがす
抄録
Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human’s consecutive motion without reference to retrial or alteration by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user’s grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.
収録刊行物
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- ヒューマンインタフェース学会論文誌
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ヒューマンインタフェース学会論文誌 21 (1), 73-84, 2019-02-25
ヒューマンインタフェース学会
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詳細情報 詳細情報について
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- CRID
- 1390564238077674752
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- NII論文ID
- 130007604239
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- NII書誌ID
- AA12557262
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- ISSN
- 2186828X
- 21868271
- 13447262
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- NDL書誌ID
- 030442964
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可